{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  \n",
    "import seaborn as sns  \n",
    "sns.set_style(\"whitegrid\")  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison among ICSE07(VSM model), DSN08(Clustering method) and our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VSM</th>\n",
       "      <th>Clustering</th>\n",
       "      <th>DBR-CNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>spark</th>\n",
       "      <td>0.587500</td>\n",
       "      <td>0.665988</td>\n",
       "      <td>0.968017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdfs</th>\n",
       "      <td>0.515464</td>\n",
       "      <td>0.665105</td>\n",
       "      <td>0.942408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mapreduce</th>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.669683</td>\n",
       "      <td>0.975904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hadoop</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                VSM  Clustering   DBR-CNN\n",
       "spark      0.587500    0.665988  0.968017\n",
       "hdfs       0.515464    0.665105  0.942408\n",
       "mapreduce  0.529412    0.669683  0.975904\n",
       "hadoop     0.400000    0.541667  0.833333"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f1=pd.DataFrame([\n",
    "                    [0.587500, 0.665988, 0.968017],\n",
    "                    [0.515464, 0.665105, 0.942408],\n",
    "                    [0.529412, 0.669683, 0.975904],\n",
    "                    [0.400000, 0.541667, 0.833333]\n",
    "                   ],\n",
    "             index=['spark','hdfs','mapreduce','hadoop'],\n",
    "             columns=pd.Index(['VSM','Clustering', 'DBR-CNN']))\n",
    "df_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115b5c790>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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ZfpMC36hygOGwjeoBbLmO21hzx6OUk9IGiXczP2oO2XiTAt+ocoDfYCW1NamC\nRIzxsmxXRGpX/AYrqY1IFSRCCHc2tD7GOCZddSRJUmuSdh6JvFo/6wMBOAaYn52qSZKkXJf20Mb3\n61oeQvgJsNM61UiSJLUa63Ktjbo8SDKMTJIktQNZCxIhhG7A6XhoQ5KkdiPtYMtVJBfrqq0MOHWd\naiRJklqNtPNI7FfrdgXwFfBhjPE/61YlSZLUWqQdbPkSQAihQ4xxVQihF7A3yYyXBglJktqJVGMk\nQgjfCiHMAYZmQsRM4DbggxDCkdmsoCRJyl1pB1teD0wD3gBOIxkb0TPz9y+yUzVJkpTr0gaJHYHr\nY4ylwKHA9BjjV8CLwDZZqpskScpxaQdbzgP6hxA2BHYBzsksHwEUNXZjIYROwE0kc1CUAhNjjL+p\np+xOmbKDSC5bdFaM8cXG7lOSJK27tD0SvwEeBd4G3ooxvhRCuIjkAz7NoY1fA7sCw4BxwKUhhNUm\ntgohdAeeBT4g6RV5BHgkhJCNi2xLkqRGShUkYow3AHsCx5F8+AO8AOweY7y/MdsKIXQFxgJnxhhn\nxRgfA64FJtRR/BTgPzHGH8YYP40x/hz4GNgtzeOQJEnrJu2hDWKMfwP+Vu32X0MIG4QQ9ogxvtGI\nTQ3M1OP1asteBS6qo+xQ4LFa9dijEfuSJElZlHZmy71IDmMMYPVejZVAp0ZsrhdQEmNcWW3ZPKBz\nCGHTGOOCasu3A94MIdwKHAL8AzgvxvhaYx+DJElad2nHSEwGPgNGAUuB0cCPgAXASY3cVldgea1l\nlbdrB5INgfOBucCBwMvAsyGErRq5T0mSlAVpg8QA4IIY4zMkk1EtjzHeRDJQ8rxGbquM1QND5e3S\nWstXAn+LMV6WGU9xAckYiRMbuU9JkpQFacdIlALlmb8/Ihnn8DTwJhAaua05QI/K6bYzy7YAlsUY\nF9Uq+6/M/qr7GMhvaAfFxcV07NixkdVqecXFxS1dhTUqLi6me/fuLV2NViPX29T2bJxcb0+wTZvK\n0qVLmT17dr3rm+p/oznbs7y8fM2FSB8kXgCuDiH8CHgNOCeEcBvJuIXaH/5r8i6wAhiS2RbAPsBb\ndZT9K7BvrWX9gPsa2kF+fj5du3ZtZLVa3pIlS1q6CmuUn59P3759W7oarUaut6nt2Ti53p5gmzaV\n2bNnN/i8NtX/RnO2Z2lpKYWFhWsslzZInAncSzI24haSS4eXkPRS/LAxG4oxLgshTAVuCSGMAbYG\nzgVOBghtF77oAAAWW0lEQVQh9AQWxxjLMvuaEEL4GUl4OBnok6mLJElqZmmv/jkXGF55O4QwDOgP\nLIoxzkmxyXNIzgJ5AVgMXJKZTwKSwxmnAFNjjEUhhANIBnteABQCB8UY/5XmcTSVoqIiSkpK1nk7\na5MEJUlqSannkQghfA04AfgGcDlJz0CqT74Y4zLg+5mf2us61Lr9Ojk8AVVRUREhFFBWVnucqCRJ\nbU/aeSR2JOk9KAJ2BiaRXCfjyBDCqBjjS9mrYutSUlKSCRH3AgXruLWngEvWvVKSJDWRtD0SNwA3\nxxgvDSH8ByDGOCaEMB/4FbB7tirYehWQXD5kXXhoQ5KU29LOIzEYmFrH8ltJ5piQJEntQNogMZ9k\nbERte5FMby1JktqBtIc2rgHuCCH8kiSMDA8hnAycDfw0W5WTJEm5Le1lxG8FfgAcTTLL5a+AA4DT\nYoxTslc9SZKUy9blMuKPA49nsS6SJKmVSXv6ZzeS2Sz7Ucclw2OMY9axXpIkqRVI2yPxB2BP4M/A\nsuxVR5IktSZpg8R+wMjMLJOSJKmdSnv6ZyHQJZsVkSRJrU/aHolTgOkhhPuBfwKrqq+MMdY1WZUk\nSWpj0gaJ04AdSC4ZXnuMRAV1z3opSZLamLRBYixwbIxxWjYrI0lSSyoqKqKkpGSN5YqLi1myZEm9\n6wsL28+1ktIGiRLgw2xWRJKkllRUVEToFyhbVtbSVWlV0gaJ8cCNIYRfAP8AVlZfGWMsWteKSZLU\nnEpKSpIQcTjQYx039nfgL1moVCuQNkg8kfn9XOZ3ReZ3XubvjutSKUlqKmvbdb022lP3dbvSA9hy\nHbeRnX+xViFtkOiT1VpIUjMoKioihALKykpbuipSm5EqSMQY/1n5dwjhJuBnMcZ2lL8ktUYlJSWZ\nEHEvUJCFLT4FXJKF7UitV+qLdlVzAvBr2lVHjqTWrQDYNQvb8dCGlHZmy+rysrANSZLUCmWjR0Jq\n0xycJ0n1W+sgEUJ4GTgkxrio2rIuwABgThPUTWpxDs6TpIY1pkdib2CDWsvmAd+MMZZnr0pS7nBw\nniQ1bF0PbTg+Qu2Eg/MkqS7ZGGwpSZLaKYOEJElKrbGHNo4KIVS/3FlH4LAQwvzqhWKMXkZckqR2\noDFBogg4t9ayecCEWssqAIOEJEntwFoHiRjjtk1YD0mS1Ao5RkKSJKVmkJAkSakZJCRJUmoGCUmS\nlJpBQpIkpWaQkCRJqRkkJElSagYJSZKUmkFCkiSlZpCQJEmpGSQkSVJqBglJkpSaQUKSJKVmkJAk\nSakZJCRJUmoGCUmSlJpBQpIkpWaQkCRJqRkkJElSagYJSZKUmkFCkiSlZpCQJEmprdfSFQAIIXQC\nbgIOB0qBiTHG36zhPtsC7wPfjTG+3OSVlCRJq8mVHolfA7sCw4BxwKUhhMPXcJ+bga5NXC9JktSA\nFg8SIYSuwFjgzBjjrBjjY8C1wIQG7nM8sGEzVVGSJNWjxYMEMJDkEMvr1Za9CuxRV+EQwqbA1cDp\nQF6T106SJNUrF4JEL6Akxriy2rJ5QOdMaKjtN8DdMcbCZqmdJEmqVy4MtuwKLK+1rPJ2p+oLQwgj\ngL2A05qhXpIkaQ1yIUiUUSswVLtdWrkghNAZuAX4YYzxq2aqmyQpxxUVFVFSUrLO2ykstKM7jVwI\nEnOAHiGEDjHGVZllWwDLYoyLqpXbHegDPBxCqD424ukQwu9ijOPq20FxcTEdO3bMesXr21d7Ulxc\nTPfu3Vu6Gk3G9mxb2lt7Qttv07lz5/Ltbx/I8uXLWroqzaI527O8vHytyuVCkHgXWAEMAV7LLNsH\neKtWuTeAHWotm01yxsefG9pBfn4+Xbs2z5miS5YsaZb95Ir8/Hz69u3b0tVoMrZn29Le2hPaR5sm\nIeJeoGAdt/YUcMm6V6oJNWd7lpaWrlUvTYsHiRjjshDCVOCWEMIYYGvgXOBkgBBCT2BxjLEM+LT6\nfUMIAHNjjOvepyVJasUKSKYjWhce2kgjF87aADgHmAm8AEwGLsnMJwHwL+Coeu5X0Qx1kyRJ9Wjx\nHglIeiWA72d+aq+rN+zEGJtn4IMkSapTrvRISJKkVsggIUmSUjNISJKk1AwSkiQpNYOEJElKzSAh\nSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk1AwS\nkiQpNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJSs0g\nIUmSUjNISJKk1AwSkiQpNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiSpNQM\nEpIkKTWDhCRJSs0gIUmSUjNISJKk1AwSkiQpNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrN\nICFJklIzSEiSpNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk1AwSkiQpNYOEJElKbb2WrgBACKETcBNw\nOFAKTIwx/qaest8FrgD6Ap8Al8QYH2+uukqSpP/KlR6JXwO7AsOAccClIYTDaxcKIewMPAzcAQwE\nbgMeCiHs1HxVlSRJlVq8RyKE0BUYCxwQY5wFzAohXAtMAKbXKn4s8HyM8cbM7ZtCCIcARwHvN1ed\nJUlSosWDBEnPwnrA69WWvQpcVEfZu4EN6lj+texXS5IkrUkuHNroBZTEGFdWWzYP6BxC2LR6wZio\n6nkIIQwA9gf+3Cw1lSRJNeRCkOgKLK+1rPJ2p/ruFELoQTJe4pUY4x+bqG6SJKkBuRAkylg9MFTe\nLq3rDiGEnsALQAVwZNNVTZIkNSQXxkjMAXqEEDrEGFdllm0BLIsxLqpdOISwFUmIKAeGxRgXrGkH\nxcXFdOzYMZt1bnBf7UlxcTHdu3dv6Wo0GduzbWlv7Qm2aVvTnO1ZXl6+VuVyIUi8C6wAhgCvZZbt\nA7xVu2DmDI8/ZcrvF2OcvzY7yM/Pp2vXrtmp7RosWbKkWfaTK/Lz8+nbt29LV6PJ2J5tS3trT7BN\n25rmbM/S0lIKCwvXWK7Fg0SMcVkIYSpwSwhhDLA1cC5wMlQdxlgcYywDfgr0IZlvokNmHSS9F+3r\nv0mSpByQC2MkAM4BZpIcsphMMlvlY5l1/yKZJwKSmS+7AG8Ac6v9XN+stZUkSUAO9EhA0isBfD/z\nU3tdh2p/FzRnvSRJUsNypUdCkiS1QgYJSZKUmkFCkiSlZpCQJEmpGSQkSVJqBglJkpSaQUKSJKVm\nkJAkSakZJCRJUmoGCUmSlJpBQpIkpWaQkCRJqRkkJElSagYJSZKUmkFCkiSlZpCQJEmpGSQkSVJq\nBglJkpSaQUKSJKVmkJAkSakZJCRJUmoGCUmSlJpBQpIkpWaQkCRJqRkkJElSagYJSZKUmkFCkiSl\nZpCQJEmpGSQkSVJqBglJkpSaQUKSJKVmkJAkSakZJCRJUmoGCUmSlJpBQpIkpWaQkCRJqRkkJElS\nagYJSZKUmkFCkiSlZpCQJEmpGSQkSVJqBglJkpSaQUKSJKVmkJAkSakZJCRJUmoGCUmSlJpBQpIk\npWaQkCRJqRkkJElSagYJSZKUmkFCkiSlZpCQJEmpGSQkSVJq67V0BQBCCJ2Am4DDgVJgYozxN/WU\n3QW4GdgJ+AD4YYzxneaqqyRJ+q9c6ZH4NbArMAwYB1waQji8dqEQQlfgSeClTPnXgSdDCF2ar6qS\nJKlSiweJTDgYC5wZY5wVY3wMuBaYUEfxY4DSGOP5MXE28B/gyOarsSRJqtTiQQIYSHKI5fVqy14F\n9qij7B6ZddXNAPZsmqpJkqSG5EKQ6AWUxBhXVls2D+gcQti0jrJzay2bB2zdhPWTJEn1yIXBll2B\n5bWWVd7utJZla5er1Blg2bJl61K/RqmoqCCEAHwKVKzj1hYAATqy+qNOo2uyuWzU7B/JpqioqKC0\ntHSdq5arstuekNU2tT0brb20J9im6die1VX77OzcULm8iopsPMT0QghHADfEGLestqwf8CGwaYxx\nUbXlTwDvxRgvqrbsaqBfjPF7tbc9c+bM44D7mrL+kiS1cccPGjTo/vpW5kKPxBygRwihQ4xxVWbZ\nFsCy6iGiWtktai3bAvhXPdt+Bjge+Awoy051JUlqFzoD25J8ltYrF4LEu8AKYAjwWmbZPsBbdZT9\nK3B+rWXfAq6oa8ODBg1aANSboiRJUoNeW1OBFj+0ARBCuJkkEIwhGTh5N3ByjPGxEEJPYHGMsSyE\nsBHwd+D3wG3AD4AjgL4xxuYbCCFJkoDcOGsD4BxgJvACMBm4JDOfBCSHLY4CiDH+BxgF7Au8DewO\nfMcQIUlSy8iJHglJktQ65UqPhJpJCGFoCGHVmksqm0II24QQVoUQeq9l+b+EEH6W+btTCOHREEJp\nCOGFpq2pWpKvz+xq7OtuLba3KoSwbza21ZbkwmBLNT+7oVpG2uf9QODbwF7Uf4aS2g5fn9nl89nE\nDBJS7tsYmBdjfLelKyJJtRkkckgI4UySgac9gfeBH5O00b0kFzK7lORU2ckxxl9m7rM+cA3JgNTN\nSeba+GWM8fbM+n8A04CTSL7Nnltrn78huXz73jHG/2vih9je5QGHhxAmkEz3/mfgpBjj4hDCYcDV\nwJYkZy11BAghnAzclfm7HPg+8BfgDpIeilKS9j2n1jTzygghbEMyMeAo4EagB/Bb4HaS57qA5Dk9\nBviKNb+eric5w2x74EVgbIxxXghhaGZ7TwPHAVfGGH8VQjiD5LT1zUhOaz8zxvhBZnsbkZyB9l2S\n6f/vqKPe28YYizLLLgWGxRj3y9w+ELgS6Ad8DJwbY3whs+4wklPjtyV5P/mfGOPL6/6MtjoNve5O\nJXlP3A5YQvJa+lGMsQIgc3hxfGYbF1TfaAihE/AL4Fjg68DzwPjK99EQwlbAdcD+wCqSqQjOizGu\nyLyuTyW5dtR4YCFweYzxt032LDQhx0jkiBDCN0nCwg9IZkJ9BXiApI16AieS/EOeAfxPCGFs5q4X\nAt8BDgO+QfJGNiWEsFm1zR8HjABOoVo3XwjhHJIJu0YaIprNSSQfUsOAQcD5IYQCkjewGzPL1gf2\nzpT/A3A2UEwy+doDJGc2/QfYGTgUGE3ypqSGnQ8cTPJcnQlMzywbSXLhv1NZu9fTz0lC3x4kEyE/\nVG3dNiRT9u8K/D6EcDBQ+WH0TZLX9QshhK9lyt+a2c8+wI+oFfSpu1u+8kNuAPDHzP53JvlfeTSE\nsHkIYWCm7r8AdiL5MvJUCGG7NT1JbVRdr7t9gUkkAWEHkvfWsSSvKUIIp5P8n5xC8v45lprtcSvw\nPeAEknmQ1gcey9x3fZJw2oWkbY8kCYvXVrv/7iQXrRwCXAbcFEIYkc0H3Vzskcgd25Kk1qIYY1EI\n4RLgCZIg0REYk/kWMyuEcD3JP/1vSSb0+nOM8S2omjL8UpI3p/mZbd8bY/zfzPqhmd9HkbzB7Rdj\n/HvzPEQBP4kxvgMQQniA/1799qUY4w2Z5RNIvj0TY1weQlgMlMcY52fWb0tyunRxjPEfIYSDgC+a\n/ZG0Pr/IvIY+CCFMAu6v9u39zyTf6p9mza+n38YYf59ZPwb4NITQP7OuArg6xvhpZv39JD0aT2fW\nXxpC+C5wQgjhHpIPmKExxlmZ8r8Apqzl4xkDvBpjvCpz+5oQQleSQ2HnArfFGKdl1k0JIQwDfgj8\nZC2335bU9bp7kOR9tXKqgaIQwt+AAcCjJMHyN5Vtl+m9+DDz98YkAeKAyl6eEMLxQHEIYSTJjJC9\ngN1ijEuA/w0hjAf+GEL4aWZ/5cCJMcYFmfVDgdNJekxaFXskcsczJN2PH4QQZgLnAR8BK4EvK7tC\nM94m6Y4lxvhHoGsI4deZa5H8g+TNrGO18p/V2lceSXf5KpKuWzWPCpJr+FRaTPKNpT9JIAQgc4ii\nofEQ15K8ic3PfFBVdX2rXhUkr41Ky4B/1rrdKfN66raG11PVTH8xxs9IuqULqq2vvt0C4NoQwn8q\nf0h6D76R+ekAzKpWvq4ZfesTSAJllRjjpTHGjzP7nVBrv6NIvnm3N3W97jrHGP8GvBdC+HkI4cEQ\nwkckvQSVbd2fam0TYywElmZufoPkffTNauu/ACLJc18AfJwJEZVeI/nS0Ddze3YmRFSqel9vbQwS\nOSLGuCzGuAewH0mX2CkkbxJbkYSJ6jqShABCCFcA95Ac2/0dSXdrXq3yta8zUkHyQRSBiVl7EFob\n5bVu55G0R+02+6q+DcQY7wfySbrlNwQezHyTVcNqv45WO80yhHA5a349rah1u+r1CBBjrN526wFn\nkXwDrvzpB1xerUz17Ve/b12HNar3IteuR+1y19Tab3+SHon2aLXXXQjh2yTvsT2Bp0gOEdaeDrq+\ntq/v2k0dMz91re+Y2V5lUGnw/6g1MUjkiBDCkBDCRTHGl2KM55G82XQhefPbuNZ50IOB9zJ/nwFM\niDFeFGN8ENgos7z2C6CGGOMjJG9wx4YQ9m6orJpUBfAByTchAEIIeSRv/HXKhMctYoy3xRgPAS4h\neRPUuskjGaM0fg2vp29W/hFC6At057+vx9oikB9j/LTyB7iY5Lh4JHl9D65Wftdqf3+V2e9G1ZZV\nH+Pwd2r9n4QQZmQOW0agT639/oBk/IeS5/U0ksNUP4wx3kXynG3Pf9v6A6q1TeaQ4saZm5+QhJMh\n1dZvStLjEzM/38gcAqm0F0l4+CRzu2/mUFSl3aj//yinOUYidywjOX46j+QY2TCgG7AJyT/27ZnB\nkf1IBmWdnrnfAuDgEMI7JL0X15N8OHVa0w5jjG9mjtPeFEL4ZrWrr6pp1BfubgfOCiFcCDxM8obf\n0AQ6/UiOeY8n+QZzEPBONivaBjUYrKspAQ5Zw+vprBDCuySHMCYDz8YYPwkhbF3H9n5D8tr9O8m3\n3TNIxkVcGWP8TwhhKjA5M9aiK8l4jErzSAbZ/iSEcBkwlGTAXmVb3wJ8GEI4G3icZDBhf+DlTN1e\nCSG8DTwJHEIyaHf4Wj4PbUl9bV8CfCuEsCNJG19IMqC5sq0nAzeGEGaRnBFzPZmejRjj0hDC7SSv\nw9NJxihdQ/K8P5cp9ylwT+Z1vRlwA3BfjHFJCAGSgHhLCOFKkss+HEHSI93q2CORIzKDrb5PMhCq\nkGQk8fEk4yQgGQT2KsnpRBdUG0T1fZJvSB8Ad5KM/n8T2CWzfk2TsVxA8qF1ZlYeiBpSZ1tkvi0e\nTHJ2zd/4b1drfX4A/Jvk1MPXgP8j6V1S/Wo/9/WdDTGGhl9PkJwNcRXJ63EOyWmjdYoxPgD8lOTs\nifdJPihGxRgrv5X+iKQNnyUZt3RDtftW1md3kkF+o6l2pePM/81okrMJ3ic5jXtUjPHfMcY3SA5f\njsvc91TgmBjjq/XVtQ2rr61/DnwOvE4yRq0UuJlMW8cY7yMJdpNJwtkz1BzUfB5JaHiI5GycpSRn\nwK3IfCk7JFPurySnfj5C8tqtVERySv7bmW0dH2P867o91JbhtTZyXGYk7wsxxo5rLCypSWXmkbg0\nxji1peui1iszj8SlMcY2cTquPRKSJCk1g4QkrT27cKVaPLQhSZJSs0dCkiSlZpCQJEmpGSQkSVJq\nBglJkpSaQUKSJKVmkJAkSakZJCRJUmoGCUmSlJpBQpIkpfb/TtYULXX4ZR0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115967d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_f1.plot(kind='bar', use_index = ['spark','hdfs','mapreduce','hadoop'], \\\n",
    "           rot = 360, ylim=(0,1.2), figsize=(6,5)).set_ylabel('F-measure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VSM</th>\n",
       "      <th>Clustering</th>\n",
       "      <th>DBR-CNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>spark</th>\n",
       "      <td>0.697710</td>\n",
       "      <td>0.499237</td>\n",
       "      <td>0.977134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdfs</th>\n",
       "      <td>0.670175</td>\n",
       "      <td>0.498246</td>\n",
       "      <td>0.961404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mapreduce</th>\n",
       "      <td>0.677852</td>\n",
       "      <td>0.510067</td>\n",
       "      <td>0.973333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hadoop</th>\n",
       "      <td>0.657143</td>\n",
       "      <td>0.371429</td>\n",
       "      <td>0.853333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                VSM  Clustering   DBR-CNN\n",
       "spark      0.697710    0.499237  0.977134\n",
       "hdfs       0.670175    0.498246  0.961404\n",
       "mapreduce  0.677852    0.510067  0.973333\n",
       "hadoop     0.657143    0.371429  0.853333"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_acc=pd.DataFrame([\n",
    "                    [0.697710, 0.499237, 0.977134],\n",
    "                    [0.670175, 0.498246, 0.961404],\n",
    "                    [0.677852, 0.510067, 0.973333],\n",
    "                    [0.657143, 0.371429, 0.853333]\n",
    "                   ],\n",
    "             index=['spark','hdfs','mapreduce','hadoop'],\n",
    "             columns=pd.Index(['VSM','Clustering', 'DBR-CNN']))\n",
    "df_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115f56550>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LE0MIY4DuJKeBEkK4H3gyxnh9ps0XwBfZLFRqWn6DlaSapDm0cTHwM+DnlRY/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\nkJAkSakZJCRJUmoGCUmSlJpBQpIkpfb/epd7BcLzCrsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c06090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_acc.plot(kind='bar', use_index = ['spark','hdfs','mapreduce','hadoop'], \\\n",
    "            rot = 360,ylim=(0,1.2), figsize=(6,5)).set_ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison between Traditional CNN and our model using different word vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Traditional-CNN</th>\n",
       "      <th>DBR-CNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>spark</th>\n",
       "      <td>0.796</td>\n",
       "      <td>0.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdfs</th>\n",
       "      <td>0.829</td>\n",
       "      <td>0.974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mapreduce</th>\n",
       "      <td>0.712</td>\n",
       "      <td>0.924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hadoop</th>\n",
       "      <td>0.662</td>\n",
       "      <td>0.678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Traditional-CNN  DBR-CNN\n",
       "spark                0.796    0.950\n",
       "hdfs                 0.829    0.974\n",
       "mapreduce            0.712    0.924\n",
       "hadoop               0.662    0.678"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f1_random = pd.DataFrame([\n",
    "                    [0.796, 0.950],\n",
    "                    [0.829, 0.974],\n",
    "                    [0.712, 0.924],\n",
    "                    [0.662, 0.678]\n",
    "                   ],\n",
    "             index=['spark','hdfs','mapreduce','hadoop'],\n",
    "             columns=pd.Index(['Traditional-CNN', 'DBR-CNN']))\n",
    "\n",
    "df_f1_random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x116fbe390>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UA5WZv18jGecwB3gOCM1c1xKgZ/V025myrYFVMcYPsur+O7O92v4JFDe2gdLS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\nSZJSM0hIkqTU/h9ZTn29x1yYggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116df4ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_f1_random. plot(kind='bar', use_index = ['spark','hdfs','mapreduce','hadoop'], \\\n",
    "            rot = 360,ylim=(0,1.2), figsize=(6,5)).set_ylabel('F-measure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Traditional-CNN</th>\n",
       "      <th>DBR-CNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>spark</th>\n",
       "      <td>0.822</td>\n",
       "      <td>0.964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdfs</th>\n",
       "      <td>0.848</td>\n",
       "      <td>0.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mapreduce</th>\n",
       "      <td>0.782</td>\n",
       "      <td>0.946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hadoop</th>\n",
       "      <td>0.726</td>\n",
       "      <td>0.752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Traditional-CNN  DBR-CNN\n",
       "spark                0.822    0.964\n",
       "hdfs                 0.848    0.982\n",
       "mapreduce            0.782    0.946\n",
       "hadoop               0.726    0.752"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_acc_random = pd.DataFrame([\n",
    "                    [0.822, 0.964],\n",
    "                    [0.848, 0.982],\n",
    "                    [0.782, 0.946],\n",
    "                    [0.726, 0.752]\n",
    "                   ],\n",
    "             index=['spark','hdfs','mapreduce','hadoop'],\n",
    "             columns=pd.Index(['Traditional-CNN', 'DBR-CNN']))\n",
    "\n",
    "df_acc_random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1171c8250>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z7R9AcWMbKC0tpbCwMEXTWq60tLRNtpMvSktL6dmzZ3s3o9W4PzuWzrY/wX3a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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1171a3e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_acc_random.plot(kind='bar', use_index = ['spark','hdfs','mapreduce','hadoop'], \\\n",
    "            rot = 360,ylim=(0,1.2), figsize=(6,5)).set_ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Traditional-CNN</th>\n",
       "      <th>DBR-CNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>spark</th>\n",
       "      <td>0.835</td>\n",
       "      <td>0.961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdfs</th>\n",
       "      <td>0.852</td>\n",
       "      <td>0.935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mapreduce</th>\n",
       "      <td>0.697</td>\n",
       "      <td>0.924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hadoop</th>\n",
       "      <td>0.850</td>\n",
       "      <td>0.962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Traditional-CNN  DBR-CNN\n",
       "spark                0.835    0.961\n",
       "hdfs                 0.852    0.935\n",
       "mapreduce            0.697    0.924\n",
       "hadoop               0.850    0.962"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f1_Glove = pd.DataFrame([\n",
    "                    [0.835, 0.961],\n",
    "                    [0.852, 0.935],\n",
    "                    [0.697, 0.924],\n",
    "                    [0.850, 0.962]\n",
    "                   ],\n",
    "             index=['spark','hdfs','mapreduce','hadoop'],\n",
    "             columns=pd.Index(['Traditional-CNN', 'DBR-CNN']))\n",
    "\n",
    "df_f1_Glove"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x117390ad0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K7JNVNhC4o7EVFBcXU1RU1MxmpbNy5cpWWU++KC4uZsCAAW3djBbj9uxYOtv2\nBLdpR9Oa27O8vJzFixc3WS9tkJgE3E4yNmImya3Dy0h6KU5szoJijKtDCLOAmSGE44HtgB8DxwCE\nEPoAK2KMFZl1TQwhnEcSHo4Bts+0RZIktbK0d/98B9i3ejqEMBoYDLwfY1yaYpGnk5wFMh9YAZyb\nuZ4EJIczjgVmxRhLQggHkAz2PAdYDHwjxvivNK9DkiRtmNTXkQghbAYcBewIXETSM9B0H0g9Yoyr\ngeMyj+x5XbKmn8ILUEmSlBfS3kZ8J5L7XBxHciijF8l9Ml4MIYzKXfMkSVI+S3v659XA9THGL5G5\n5kOM8XiSwxM/z1HbJElSnksbJHYFZtVTfgPJNSYkSVInkDZIvEcyNiLbSJLLW0uSpE4g7WDLqcD/\nhRB+RhJG9g0hHAOcCvxvrhonSZLyW9rbiN8A/BA4jOQqlz8HDgBOiDFem7vmSZKkfLYhtxF/AHgg\nh22RJEntTNrbiPckuZrlQOq5ZXjmDA5JktTBpe2R+DWwJ/BnYHXumiNJktqTtEFiDPDVzFUmJUlS\nJ5X29M/FQI9cNkSSJLU/aXskjgXmhBDuBN4C1taeGWOs72JVkiSpg0kbJE4AvkByn43sMRJV1H/V\nS0mS1MGkDRLjgcNjjLNz2RhJktS+pB0jUQa8nMuGSJKk9idtj8TJwIwQwk+BN4FPas+MMZZsaMMk\nSVL+Sxskfpf590+Zf6sy/xZk/l+4IY2SJEntQ9ogsX1OWyFJktqlVEEixvhW9f9DCNcB58UYy3LW\nKkmS1C6kHWxZ21FArxwsR5IktTO5CBIFOViGJElqh3IRJCRJUie13kEihLAghLB5VlkPYAjJZbIl\nSVIn05weib2AjbPKlgEbxRgrc9ckSZLUXmzooQ3HR0iS1Ik5RkKSJKVmkJAkSak194JUY0MIK2tN\nFwLfDiG8V7tSjNHbiEuS1Ak0J0iUAD/OKlsGTMwqqwIMEpIkdQLrHSRijP1bsB2SJKkdcoyEJElK\nzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk\n1AwSkiQpNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJ\nSs0gIUmSUtuorRsAEELoBlwHHAKUA9NijFc28Zz+wN+Bb8YYF7R4IyVJ0jrypUfiCmA4MBo4CTg/\nhHBIE88ejJD5AAAR9ElEQVS5Hihq4XZJkqRGtHmQCCEUAeOBSTHGRTHG3wCXAxMbec6RwCat1ERJ\nktSANg8SwM4kh1ieqlX2OLB7fZVDCFsClwHfBwpavHWSJKlB+RAktgHKYoyf1CpbBnTPhIZsVwK/\nijEubpXWSZKkBuXDYMsi4KOssurpbrULQwj7ASOBE1qhXZIkqQn5ECQqyAoMtabLqwtCCN2BmcCJ\nMcY1rdQ2SZLUiHwIEkuB3iGELjHGtZmyrYHVMcb3a9XbDdgeuC+EUHtsxLwQwq0xxpMaWkFpaSmF\nhYU5b3hD6+pMSktL6dWrV1s3o8W4PTuWzrY9wW3a0bTm9qysrFyvevkQJF4EPgb2AJ7MlO0NPJtV\n72ngC1llr5Gc8fHnxlZQXFxMUVHrnCm6cuXKVllPviguLmbAgAFt3YwW4/bsWDrb9gS3aUfTmtuz\nvLycxYubHo7Y5kEixrg6hDALmBlCOB7YDvgxcAxACKEPsCLGWAG8Ufu5IQSAd2KMZa3bakmSBPlx\n1gbA6cBCYD5wDXBu5noSAP8CxjbwvKpWaJskSWpAm/dIQNIrARyXeWTPazDsxBhbZ+CDJEmqV770\nSEiSpHbIICFJklIzSEiSpNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk1AwSkiQpNYOEJElKzSAhSZJS\nM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk1AwSkiQp\nNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJSs0gIUmS\nUjNISJKk1AwSkiQpNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiSpNQMEpIk\nKTWDhCRJSs0gIUmSUjNISJKk1AwSkiQpNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJ\nklIzSEiSpNQMEpIkKTWDhCRJSm2jtm4AQAihG3AdcAhQDkyLMV7ZQN1vAhcDA4DXgXNjjA+0Vlsl\nSdKn8qVH4gpgODAaOAk4P4RwSHalEMJQ4D7g/4CdgRuBe0MIX2y9pkqSpGpt3iMRQigCxgMHxBgX\nAYtCCJcDE4E5WdUPBx6OMc7ITF8XQvgWMBb4e2u1WZIkJdo8SJD0LGwEPFWr7HFgSj11fwVsXE/5\nZrlvliRJako+HNrYBiiLMX5Sq2wZ0D2EsGXtijFR0/MQQhgCfAX4c6u0VJIk1ZEPQaII+CirrHq6\nW0NPCiH0Jhkv8ViM8bct1DZJktSIfAgSFawbGKqny+t7QgihDzAfqAK+23JNkyRJjcmHMRJLgd4h\nhC4xxrWZsq2B1THG97MrhxC2JQkRlcDoGOPyplZQWlpKYWFhLtvc6Lo6k9LSUnr16tXWzWgxbs+O\npbNtT3CbdjStuT0rKyvXq14+BIkXgY+BPYAnM2V7A89mV8yc4fGHTP0xMcb31mcFxcXFFBUV5aa1\nTVi5cmWrrCdfFBcXM2DAgLZuRotxe3YsnW17gtu0o2nN7VleXs7ixYubrNfmQSLGuDqEMAuYGUI4\nHtgO+DFwDNQcxlgRY6wA/hfYnuR6E10y8yDpvehcf02SJOWBfBgjAXA6sJDkkMU1JFer/E1m3r9I\nrhMByZUvewBPA+/UelzVqq2VJElAHvRIQNIrARyXeWTP61Lr/4Nas12SJKlx+dIjIUmS2iGDhCRJ\nSs0gIUmSUjNISJKk1AwSkiQpNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiS\npNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk1AwSkiQpNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4Qk\nSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk1AwSkiQpNYOEJElKzSAhSZJSM0hI\nkqTUDBKSJCk1g4QkSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk1AwSkiQpNYOE\nJElKzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJSs0gIUmSUjNI\nSJKk1AwSkiQptY3augEAIYRuwHXAIUA5MC3GeGUDdYcB1wNfBF4CTowxPt9abZUkSZ/Klx6JK4Dh\nwGjgJOD8EMIh2ZVCCEXAg8CjmfpPAQ+GEHq0XlMlSVK1Ng8SmXAwHpgUY1wUY/wNcDkwsZ7q3wPK\nY4xnx8SpwAfAd1uvxZIkqVqbBwlgZ5JDLE/VKnsc2L2eurtn5tX2BLBnyzRNkiQ1Jh+CxDZAWYzx\nk1ply4DuIYQt66n7TlbZMmC7FmyfJElqQD4MtiwCPsoqq57utp51s+tV6w6wevXqDWlfs1RVVRFC\nAN4AqlpprcuBAIWs++60lMJklVVVVZSXl7fSSluf27NjaZvtCW7TluM+2nJqfXd2b6xeQVVVa+5M\n6wohfAe4OsbYt1bZQOBlYMsY4/u1yn8H/C3GOKVW2WXAwBjjwdnLXrhw4RHAHS3ZfkmSOrgjR4wY\ncWdDM/OhR2Ip0DuE0CXGuDZTtjWwunaIqFV366yyrYF/NbDsPwJHAkuAitw0V5KkTqE70J/ku7RB\n+RAkXgQ+BvYAnsyU7Q08W0/dvwJnZ5V9Gbi4vgWPGDFiOdBgipIkSY16sqkKbX5oAyCEcD1JIDie\nZODkr4BjYoy/CSH0AVbEGCtCCJsCrwJ3ATcCPwS+AwyIMbbeQAhJkgTkx1kbAKcDC4H5wDXAuZnr\nSUBy2GIsQIzxA+BAYB/gOWA34OuGCEmS2kZe9EhIkqT2KV96JNRKQgijQghrm66pXAohfC6EsDaE\n0G896/8lhHBe5v/dQgj3hxDKQwjzW7alakvun7nV3P1uPZa3NoSwTy6W1ZHkw2BLtT67odpG2vf9\na8D+wEgaPkNJHYf7Z275frYwg4SU/zYHlsUYX2zrhkhSNoNEHgkhTCIZeNoH+DtwGsk2up3kRmbn\nk5wqe02M8WeZ53QFppIMSP0sybU2fhZjvCkz/01gNjCO5Nfsj7PWeSXJ7dv3ijG+3cIvsbMrAA4J\nIUwkudz7n4FxMcYVIYRvA5cBfUnOWioECCEcA9yS+X8lcBzwF+D/SHooykm27+lZl5lXRgjhc8Cb\nJAO1ZwC9gV8CN5G814NI3tPvAWtoen+6iuQMsx2AR4DxMcZlIYRRmeXNA44ALokx/jyE8AOS09a3\nIjmtfVKM8aXM8jYlOQPtmySX//+/etrdP8ZYkik7HxgdYxyTmf4acAkwEPgn8OMY4/zMvG+TnBrf\nn+Tz5KwY44INf0fbncb2uwkkn4mfB1aS7EunxBirADKHF0/OLOOc2gsNIXQDfgocDnwGeBg4ufpz\nNISwLTAd+AqwluRSBGfEGD/O7NcTSO4ddTLwH+CiGOMvW+xdaEGOkcgTIYRdSMLCD4EAPAbcTbKN\n+gBHk/xB/gA4K4QwPvPUycDXgW8DO5J8kF0bQtiq1uKPAPYDjqVWN18I4XSSC3Z91RDRasaRfEmN\nBkYAZ4cQBpF8gM3IlHUF9srU/zVwKlBKcvG1u0nObPoAGAocBBxK8qGkxp0N/A/JezUJmJMp+yrJ\njf8msH770wUkoW93ksv231tr3udILtk/HLgrhPA/QPWX0S4k+/X8EMJmmfo3ZNazN3AKWUGf+rvl\nq7/khgC/zax/KMnfyv0hhM+GEHbOtP2nwBdJfoz8PoTw+abepA6qvv1uH+AXJAHhCySfreNJ9ilC\nCN8n+Ts5luTzczx1t8cNwMHAUSTXQeoK/Cbz3K4k4bQHybb9LklYvLzW83cjuWnlHsCFwHUhhP1y\n+aJbiz0S+aM/SWotiTGWhBDOBX5HEiQKgeMzv2IWhRCuIvmj/yXJBb3+HGN8FmouGX4+yYfTe5ll\n3x5j/Edm/qjMv2NJPuDGxBhfbZ2XKODMGOPzACGEu/n07rePxhivzpRPJPn1TIzxoxDCCqAyxvhe\nZn5/ktOlS2OMb4YQvgH8t9VfSfvz08w+9FII4RfAnbV+vf+Z5Ff9PJren34ZY7wrM/944I0QwuDM\nvCrgshjjG5n5d5L0aMzLzD8/hPBN4KgQwm0kXzCjYoyLMvV/Cly7nq/neODxGOOlmempIYQikkNh\nPwZujDHOzsy7NoQwGjgROHM9l9+R1Lff3UPyuVp9qYGSEMILwBDgfpJgeWX1tsv0Xryc+f/mJAHi\ngOpenhDCkUBpCOGrJFeE3Ab4UoxxJfCPEMLJwG9DCP+bWV8lcHSMcXlm/ijg+yQ9Ju2KPRL5448k\n3Y8vhRAWAmcArwCfAB9Wd4VmPEfSHUuM8bdAUQjhisy9SN4k+TArrFV/Sda6Cki6y9eSdN2qdVSR\n3Fmo2gqSXyyDSQIhAJlDFI2Nh7ic5EPsvcwXVU3XtxpURbJvVFsNvJU13S2zP/VsYn+qudJfjHEJ\nSbf0oFrzay93EHB5COGD6gdJ78GOmUcXYFGt+vVd0bchgSRQ1ogxnh9j/GdmvROz1nsgyS/vzqa+\n/a57jPEF4G8hhAtCCPeEEF4h6SWo3taDqbVtYoyLgVWZyR1JPkefqTX/v0Akee8HAf/MhIhqT5L8\naBiQmX4tEyKq1XyutzcGiTwRY1wdY9wdGEPSJXYsyYfEtiRhorZCkhBACOFi4DaSY7u3knS3FmTV\nz77PSBXJF1EEpuXsRWh9VGZNF5Bsj+xttqahBcQY7wSKSbrlNwHuyfySVeOy96N1TrMMIVxE0/vT\nx1nTNfsjQIyx9rbbCPgRyS/g6sdA4KJadWovv/Zz6zusUbsXObsd2fWmZq13MEmPRGe0zn4XQtif\n5DO2D/B7kkOE2ZeDbmjbN3TvpsLMo775hZnlVQeVRv+O2hODRJ4IIewRQpgSY3w0xngGyYdND5IP\nv82zzoPeFfhb5v8/ACbGGKfEGO8BNs2UZ+8AdcQY55J8wB0eQtirsbpqUVXASyS/hAAIIRSQfPDX\nKxMet44x3hhj/BZwLsmHoDZMAckYpZOb2J92qf5PCGEA0ItP98dsESiOMb5R/QB+QnJcPJLs37vW\nqj+81v/XZNa7aa2y2mMcXiXr7ySE8ETmsGUEts9a7w9Jxn8oeV9PIDlMdWKM8RaS92wHPt3WL1Fr\n22QOKW6emXydJJzsUWv+liQ9PjHz2DFzCKTaSJLw8HpmekDmUFS1L9Hw31Fec4xE/lhNcvx0Gckx\nstFAT2ALkj/smzKDIweSDMr6fuZ5y4H/CSE8T9J7cRXJl1O3plYYY3wmc5z2uhDCLrXuvqqW0VC4\nuwn4UQhhMnAfyQd+YxfQGUhyzPtkkl8w3wCez2VDO6BGg3UtZcC3mtiffhRCeJHkEMY1wEMxxtdD\nCNvVs7wrSfbdV0l+7f6AZFzEJTHGD0IIs4BrMmMtikjGY1RbRjLI9swQwoXAKJIBe9Xbeibwcgjh\nVOABksGEg4EFmbY9FkJ4DngQ+BbJoN191/N96Ega2vZlwJdDCDuRbOPJJAOaq7f1NcCMEMIikjNi\nriLTsxFjXBVCuIlkP/w+yRilqSTv+58y9d4Absvs11sBVwN3xBhXhhAgCYgzQwiXkNz24TskPdLt\njj0SeSIz2Oo4koFQi0lGEh9JMk4CkkFgj5OcTnROrUFUx5H8QnoJuJlk9P8zwLDM/KYuxnIOyZfW\npJy8EDWm3m2R+bX4PyRn17zAp12tDfkh8C7JqYdPAm+T9C6pYdnvfUNnQxxP4/sTJGdDXEqyPy4l\nOW20XjHGu4H/JTl74u8kXxQHxhirf5WeQrINHyIZt3R1redWt2c3kkF+h1LrTseZv5tDSc4m+DvJ\nadwHxhjfjTE+TXL48qTMcycA34sxPt5QWzuwhrb1BcC/gadIxqiVA9eT2dYxxjtIgt01JOHsj9Qd\n1HwGSWi4l+RsnFUkZ8B9nPlR9q1Mvb+SnPo5l2TfrVZCckr+c5llHRlj/OuGvdS24b028lxmJO/8\nGGNhk5UltajMdSTOjzHOauu2qP3KXEfi/Bhjhzgd1x4JSZKUmkFCktafXbhSFg9tSJKk1OyRkCRJ\nqRkkJElSagYJSZKUmkFCkiSlZpCQJEmpGSQkSVJqBglJkpSaQUKSJKVmkJAkSan9P3eUg6wy+Qr7\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c0e190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_f1_Glove.plot(kind='bar', use_index = ['spark','hdfs','mapreduce','hadoop'], \\\n",
    "           rot = 360, ylim=(0,1.2), figsize=(6,5)).set_ylabel('F-measure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Traditional-CNN</th>\n",
       "      <th>DBR-CNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>spark</th>\n",
       "      <td>0.857</td>\n",
       "      <td>0.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdfs</th>\n",
       "      <td>0.882</td>\n",
       "      <td>0.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mapreduce</th>\n",
       "      <td>0.753</td>\n",
       "      <td>0.946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hadoop</th>\n",
       "      <td>0.908</td>\n",
       "      <td>0.989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Traditional-CNN  DBR-CNN\n",
       "spark                0.857    0.972\n",
       "hdfs                 0.882    0.957\n",
       "mapreduce            0.753    0.946\n",
       "hadoop               0.908    0.989"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_acc_Glove = pd.DataFrame([\n",
    "                    [0.857, 0.972],\n",
    "                    [0.882, 0.957],\n",
    "                    [0.753, 0.946],\n",
    "                    [0.908, 0.989]\n",
    "                   ],\n",
    "             index=['spark','hdfs','mapreduce','hadoop'],\n",
    "             columns=pd.Index(['Traditional-CNN', 'DBR-CNN']))\n",
    "\n",
    "df_acc_Glove"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x117568610>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HvP322wA89dRTDB06tE6d6m/1O+64Y53ylStXblAXYMcdd+SCCy7giCOOaHTbb775JgA7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\nhCRJSu3/A6GvsGpq+riNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116df4f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_acc_Glove.plot(kind='bar', use_index = ['spark','hdfs','mapreduce','hadoop'], \\\n",
    "            rot = 360,ylim=(0,1.2), figsize=(6,5)).set_ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Traditional-CNN</th>\n",
       "      <th>DBR-CNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>spark</th>\n",
       "      <td>0.839</td>\n",
       "      <td>0.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdfs</th>\n",
       "      <td>0.831</td>\n",
       "      <td>0.837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mapreduce</th>\n",
       "      <td>0.729</td>\n",
       "      <td>0.926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hadoop</th>\n",
       "      <td>0.843</td>\n",
       "      <td>0.946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Traditional-CNN  DBR-CNN\n",
       "spark                0.839    0.931\n",
       "hdfs                 0.831    0.837\n",
       "mapreduce            0.729    0.926\n",
       "hadoop               0.843    0.946"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f1_w2v = pd.DataFrame([\n",
    "                    [0.839, 0.931],\n",
    "                    [0.831, 0.837],\n",
    "                    [0.729, 0.926],\n",
    "                    [0.843, 0.946]\n",
    "                   ],\n",
    "             index=['spark','hdfs','mapreduce','hadoop'],\n",
    "             columns=pd.Index(['Traditional-CNN', 'DBR-CNN']))\n",
    "\n",
    "df_f1_w2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11772fd50>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7VlZWNl2J9EFiAXBZCOEU4Cng9BDCjSTjFrK//JvyEvAxsGdmWQD7AM/VU/fP\nwL5ZZQOBOxpbQXFxMUVFRc1sVjqrVq3KyXryRXFxMQMGDGjrZrQat2fH0tm2J7hNO5pcbs/y8nKW\nLFnSZL20QWIycDvJ2IhZJLcOLyPppTipOQuKMa4JIcwGZoUQTgC2B34EHAsQQugDrIwxVmTWNSmE\ncB5JeDgW2CHTFkmSlGNp7/75DrBf9XQIYTQwGHg/xrgsxSJPJzkLZAGwEjg3cz0JSA5nHAfMjjGW\nhBAOJBnseQ6wBPh6jPGfaV6HJEnaOKmvIxFC2Bw4GtgJuIikZ6DpPpB6xBjXAMdnHtnzumRNP40X\noJIkKS+kvY34ziT3uTie5FBGL5L7ZLwUQhjVcs2TJEn5LO3pn1cD18cYv0jmmg8xxhNIDk/8rIXa\nJkmS8lzaILEbMLue8htIrjEhSZI6gbRB4j2SsRHZRpJc3lqSJHUCaQdbTgP+L4TwU5Iwsl8I4Vjg\nVOB/W6pxkiQpv6W9jfgNwA+Aw0mucvkz4EDgxBjjtS3XPEmSlM825jbiDwAPtGBbJElSO5P2NuI9\nSa5mOZB6bhmeOYNDkiR1cGl7JH4F7AU8DKxpueZIkqT2JG2QGAN8JXOVSUmS1EmlPf1zCdCjJRsi\nSZLan7Q9EscBc0MIdwL/ANbVnhljrO9iVZIkqYNJGyROBD5Pcp+N7DESVdR/1UtJktTBpA0S44Ej\nYoxzWrIxkiSpfUk7RqIMeKUlGyJJktqftD0SJwMzQwg/Ad4CPqk9M8ZYsrENkyRJ+S9tkPht5t8/\nZv6tyvxbkPm7cGMaJUmS2oe0QWKHFm2FJElql1IFiRjjP6r/DiFcB5wXYyxrsVZJkqR2Ie1gy9qO\nBnq1wHIkSVI70xJBoqAFliFJktqhlggSkiSpk9rgIBFCWBhC2CKrrAcwhOQy2ZIkqZNpTo/E3sCm\nWWXLgU1ijJUt1yRJktRebOyhDcdHSJLUiTlGQpIkpWaQkCRJqTX3glRjQwirak0XAt8KIbxXu1KM\n0duIS5LUCTQnSJQAP8oqWw5MyiqrAgwSkiR1AhscJGKM/VuxHZIkqR1yjIQkSUrNICFJklIzSEiS\npNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk1AwSkiQpNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4Qk\nSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk1AwSkiQpNYOEJElKzSAhSZJS26St\nGwAQQuiHuu9IAAASIUlEQVQGXAccCpQD02OMVzbxnP7AX4FvxBgXtnojJUnSevKlR+IKYDgwGpgI\nnB9COLSJ51wPFLVyuyRJUiPaPEiEEIqA8cDkGOPiGOOvgcuBSY085yhgsxw1UZIkNaDNgwSwC8kh\nlqdrlT0B7FFf5RDCVsBlwPeAglZvnSRJalA+BIltgbIY4ye1ypYD3TOhIduVwC9jjEty0jpJktSg\nfBhsWQR8lFVWPd2tdmEIYX9gJHBiDtolSZKakA9BooKswFBrury6IITQHZgFnBRjXJujtkmSpEbk\nQ5BYBvQOIXSJMa7LlG0DrIkxvl+r3u7ADsB9IYTaYyPmhxBujTFObGgFpaWlFBYWtnjDG1pXZ1Ja\nWkqvXr3auhmtxu3ZsXS27Qlu044ml9uzsrJyg+rlQ5B4CfgY2BN4KlO2D/BcVr1ngM9nlb1OcsbH\nw42toLi4mKKi3JwpumrVqpysJ18UFxczYMCAtm5Gq3F7diydbXuC27SjyeX2LC8vZ8mSpocjtnmQ\niDGuCSHMBmaFEE4Atgd+BBwLEELoA6yMMVYAb9Z+bggB4J0YY1luWy1JkiA/ztoAOB1YBCwArgHO\nzVxPAuCfwNgGnleVg7ZJkqQGtHmPBCS9EsDxmUf2vAbDTowxNwMfJElSvfKlR0KSJLVDBglJkpSa\nQUKSJKVmkJAkSakZJCRJUmoGCUmSlJpBQpIkpWaQkCRJqRkkJElSagYJSZKUmkFCkiSlZpCQJEmp\nGSQkSVJqBglJkpSaQUKSJKVmkJAkSakZJCRJUmoGCUmSlJpBQpIkpWaQkCRJqRkkJElSagYJSZKU\nmkFCkiSlZpCQJEmpGSQkSVJqBglJkpSaQUKSJKVmkJAkSakZJCRJUmoGCUmSlJpBQpIkpWaQkCRJ\nqRkkJElSagYJSZKUmkFCkiSlZpCQJEmpGSQkSVJqBglJkpSaQUKSJKVmkJAkSakZJCRJUmoGCUmS\nlJpBQpIkpWaQkCRJqRkkJElSagYJSZKUmkFCkiSlZpCQJEmpGSQkSVJqBglJkpSaQUKSJKVmkJAk\nSakZJCRJUmqbtHUDAEII3YDrgEOBcmB6jPHKBup+A7gYGAC8AZwbY3wgV22VJEn/lS89ElcAw4HR\nwETg/BDCodmVQghDgfuA/wN2AW4E7g0hfCF3TZUkSdXavEcihFAEjAcOjDEuBhaHEC4HJgFzs6of\nATwSY5yZmb4uhPBNYCzw11y1WZIkJdo8SJD0LGwCPF2r7Algaj11fwlsWk/55i3fLEmS1JR8OLSx\nLVAWY/ykVtlyoHsIYavaFWOipuchhDAE+DLwcE5aKkmS6siHIFEEfJRVVj3draEnhRB6k4yXeDzG\n+JtWapskSWpEPgSJCtYPDNXT5fU9IYTQB1gAVAHfab2mSZKkxuTDGIllQO8QQpcY47pM2TbAmhjj\n+9mVQwjbkYSISmB0jHFFUysoLS2lsLCwJdvc6Lo6k9LSUnr16tXWzWg1bs+OpbNtT3CbdjS53J6V\nlZUbVC8fgsRLwMfAnsBTmbJ9gOeyK2bO8Ph9pv6YGON7G7KC4uJiioqKWqa1TVi1alVO1pMviouL\nGTBgQFs3o9W4PTuWzrY9wW3a0eRye5aXl7NkyZIm67V5kIgxrgkhzAZmhRBOALYHfgQcCzWHMVbG\nGCuA/wV2ILneRJfMPEh6LzrX/yZJkvJAPoyRADgdWERyyOIakqtV/joz758k14mA5MqXPYBngHdq\nPa7KaWslSRKQBz0SkPRKAMdnHtnzutT6e1Au2yVJkhqXLz0SkiSpHTJISJKk1AwSkiQpNYOEJElK\nzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk\n1AwSkiQpNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJ\nSs0gIUmSUjNISJKk1AwSkiQpNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4QkSUrNICFJklIzSEiS\npNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk1AwSkiQpNYOEJElKzSAhSZJSM0hIkqTUDBKSJCk1g4Qk\nSUrNICFJklIzSEiSpNQMEpIkKTWDhCRJSs0gIUmSUjNISJKk1AwSkiQpNYOEJElKzSAhSZJS26St\nGwAQQugGXAccCpQD02OMVzZQdxhwPfAF4GXgpBjjC7lqqyRJ+q986ZG4AhgOjAYmAueHEA7NrhRC\nKAIeBB7L1H8aeDCE0CN3TZUkSdXaPEhkwsF4YHKMcXGM8dfA5cCkeqp/FyiPMZ4dE6cCHwDfyV2L\nJUlStTYPEsAuJIdYnq5V9gSwRz1198jMq+1JYK/WaZokSWpMPgSJbYGyGOMntcqWA91DCFvVU/ed\nrLLlwPat2D5JktSAfBhsWQR8lFVWPd1tA+tm16vWHWDNmjUb075mqaqqIoQAvAlU5WitK4AAhaz/\n7rSWwmSVVVVVlJeX52iluef27FjaZnuC27T1uI+2nlrfnd0bq1dQVZXLnWl9IYRvA1fHGPvWKhsI\nvAJsFWN8v1b5b4G/xBin1iq7DBgYYzwke9mLFi06ErijNdsvSVIHd9SIESPubGhmPvRILAN6hxC6\nxBjXZcq2AdbUDhG16m6TVbYN8M8Glv0H4ChgKVDRMs2VJKlT6A70J/kubVA+BImXgI+BPYGnMmX7\nAM/VU/fPwNlZZV8CLq5vwSNGjFgBNJiiJElSo55qqkKbH9oACCFcTxIITiAZOPlL4NgY469DCH2A\nlTHGihDCp4DXgLuAG4EfAN8GBsQYczcQQpIkAflx1gbA6cAiYAFwDXBu5noSkBy2GAsQY/wAOAjY\nF3ge2B34miFCkqS2kRc9EpIkqX3Klx4J5UgIYVQIYV3TNdWSQgifDSGsCyH028D6fwohnJf5u1sI\n4f4QQnkIYUHrtlRtyf2zZTV3v9uA5a0LIezbEsvqSPJhsKVyz26otpH2ff8qcAAwkobPUFLH4f7Z\nsnw/W5lBQsp/WwDLY4wvtXVDJCmbQSKPhBAmkww87QP8FTiNZBvdTnIjs/NJTpW9Jsb408xzugLT\nSAakfobkWhs/jTHelJn/FjAHGEfya/ZHWeu8kuT27XvHGN9u5ZfY2RUAh4YQJpFc7v1hYFyMcWUI\n4VvAZUBfkrOWCgFCCMcCt2T+rgSOB/4E/B9JD0U5yfY9Pesy88oIIXwWeItkoPZMoDfwC+Amkvd6\nEMl7+l1gLU3vT1eRnGG2I/AoMD7GuDyEMCqzvPnAkcAlMcafhRC+T3La+tYkp7VPjjG+nFnep0jO\nQPsGyeX//6+edvePMZZkys4HRscYx2SmvwpcAgwE/g78KMa4IDPvWySnxvcn+Tw5K8a4cOPf0Xan\nsf1uAsln4ueAVST70ikxxiqAzOHFkzPLOKf2QkMI3YCfAEcAnwYeAU6u/hwNIWwHzAC+DKwjuRTB\nGTHGjzP79QSSe0edDPwbuCjG+ItWexdakWMk8kQIYVeSsPADIACPA3eTbKM+wDEk/yG/D5wVQhif\neeoU4GvAt4CdSD7Irg0hbF1r8UcC+wPHUaubL4RwOskFu75iiMiZcSRfUqOBEcDZIYRBJB9gMzNl\nXYG9M/V/BZwKlJJcfO1ukjObPgCGAgcDh5F8KKlxZwP/Q/JeTQbmZsq+QnLjvwls2P50AUno24Pk\nsv331pr3WZJL9g8H7goh/A9Q/WW0K8l+vSCEsHmm/g2Z9ewDnEJW0Kf+bvnqL7khwG8y6x9K8n/l\n/hDCZ0IIu2Ta/hPgCyQ/Rn4XQvhcU29SB1Xffrcv8HOSgPB5ks/W8ST7FCGE75H8PzmO5PNzPHW3\nxw3AIcDRJNdB6gr8OvPcriThtAfJtv0OSVi8vNbzdye5aeWewIXAdSGE/VvyReeKPRL5oz9Jai2J\nMZaEEM4FfksSJAqBEzK/YhaHEK4i+U//C5ILej0cY3wOai4Zfj7Jh9N7mWXfHmP8W2b+qMy/Y0k+\n4MbEGF/LzUsUcGaM8QWAEMLd/Pfut4/FGK/OlE8i+fVMjPGjEMJKoDLG+F5mfn+S06VLY4xvhRC+\nDvwn56+k/flJZh96OYTwc+DOWr/eHyb5VT+fpvenX8QY78rMPwF4M4QwODOvCrgsxvhmZv6dJD0a\n8zPzzw8hfAM4OoRwG8kXzKgY4+JM/Z8A127g6zkBeCLGeGlmeloIoYjkUNiPgBtjjHMy864NIYwG\nTgLO3MDldyT17Xf3kHyuVl9qoCSE8CIwBLifJFheWb3tMr0Xr2T+3oIkQBxY3csTQjgKKA0hfIXk\nipDbAl+MMa4C/hZCOBn4TQjhfzPrqwSOiTGuyMwfBXyPpMekXbFHIn/8gaT78eUQwiLgDOBV4BPg\nw+qu0IznSbpjiTH+BigKIVyRuRfJWyQfZoW16i/NWlcBSXf5OpKuW+VGFcmdhaqtJPnFMpgkEAKQ\nOUTR2HiIy0k+xN7LfFHVdH2rQVUk+0a1NcA/sqa7Zfannk3sTzVX+osxLiXplh5Ua37t5Q4CLg8h\nfFD9IOk92Cnz6AIsrlW/viv6NiSQBMoaMcbzY4x/z6x3UtZ6DyL55d3Z1LffdY8xvgj8JYRwQQjh\nnhDCqyS9BNXbejC1tk2McQmwOjO5E8nn6LO15v8HiCTv/SDg75kQUe0pkh8NAzLTr2dCRLWaz/X2\nxiCRJ2KMa2KMewBjSLrEjiP5kNiOJEzUVkgSAgghXAzcRnJs91aS7taCrPrZ9xmpIvkiisD0FnsR\n2hCVWdMFJNsje5utbWgBMcY7gWKSbvnNgHsyv2TVuOz9aL3TLEMIF9H0/vRx1nTN/ggQY6y97TYB\nfkjyC7j6MRC4qFad2suv/dz6DmvU7kXObkd2vWlZ6x1M0iPRGa2334UQDiD5jO0D/I7kEGH25aAb\n2vYN3bupMPOob35hZnnVQaXR/0ftiUEiT4QQ9gwhTI0xPhZjPIPkw6YHyYffFlnnQe8G/CXz9/eB\nSTHGqTHGe4BPZcqzd4A6YozzSD7gjggh7N1YXbWqKuBlkl9CAIQQCkg++OuVCY/bxBhvjDF+EziX\n5ENQG6eAZIzSyU3sT7tW/xFCGAD04r/7Y7YIFMcY36x+AD8mOS4eSfbv3WrVH17r77WZ9X6qVlnt\nMQ6vkfX/JITwZOawZQR2yFrvD0jGfyh5X08kOUx1UozxFpL3bEf+u61fpta2yRxS3CIz+QZJONmz\n1vytSHp8YuaxU+YQSLWRJOHhjcz0gMyhqGpfpOH/R3nNMRL5Yw3J8dPlJMfIRgM9gS1J/mPflBkc\nOZBkUNb3Ms9bAfxPCOEFkt6Lq0i+nLo1tcIY47OZ47TXhRB2rXX3VbWOhsLdTcAPQwhTgPtIPvAb\nu4DOQJJj3ieT/IL5OvBCSza0A2o0WNdSBnyzif3phyGEl0gOYVwDPBRjfCOEsH09y7uSZN99jeTX\n7vdJxkVcEmP8IIQwG7gmM9aiiGQ8RrXlJINszwwhXAiMIhmwV72tZwGvhBBOBR4gGUw4GFiYadvj\nIYTngQeBb5IM2t1vA9+HjqShbV8GfCmEsDPJNp5CMqC5eltfA8wMISwmOSPmKjI9GzHG1SGEm0j2\nw++RjFGaRvK+/zFT703gtsx+vTVwNXBHjHFVCAGSgDgrhHAJyW0fvk3SI93u2CORJzKDrY4nGQi1\nhGQk8VEk4yQgGQT2BMnpROfUGkR1PMkvpJeBm0lG/z8LDMvMb+piLOeQfGlNbpEXosbUuy0yvxb/\nh+Tsmhf5b1drQ34AvEty6uFTwNskvUtqWPZ739DZECfQ+P4EydkQl5Lsj8tIThutV4zxbuB/Sc6e\n+CvJF8VBMcbqX6WnkGzDh0jGLV1d67nV7dmdZJDfYdS603Hm/81hJGcT/JXkNO6DYozvxhifITl8\nOTHz3AnAd2OMTzTU1g6soW19AfAv4GmSMWrlwPVktnWM8Q6SYHcNSTj7A3UHNZ9BEhruJTkbZzXJ\nGXAfZ36UfTNT788kp37OI9l3q5WQnJL/fGZZR8UY/7xxL7VteK+NPJcZybsgxljYZGVJrSpzHYnz\nY4yz27otar8y15E4P8bYIU7HtUdCkiSlZpCQpA1nF66UxUMbkiQpNXskJElSagYJSZKUmkFCkiSl\nZpCQJEmpGSQkSVJqBglJkpSaQUKSJKVmkJAkSakZJCRJUmr/DzzOf7GWqxuQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11759a490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_f1_w2v.plot(kind='bar', use_index = ['spark','hdfs','mapreduce','hadoop'], \\\n",
    "           rot = 360, ylim=(0,1.2), figsize=(6,5)).set_ylabel('F-measure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Traditional-CNN</th>\n",
       "      <th>DBR-CNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>spark</th>\n",
       "      <td>0.849</td>\n",
       "      <td>0.948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hdfs</th>\n",
       "      <td>0.852</td>\n",
       "      <td>0.895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mapreduce</th>\n",
       "      <td>0.849</td>\n",
       "      <td>0.960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hadoop</th>\n",
       "      <td>0.867</td>\n",
       "      <td>0.964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Traditional-CNN  DBR-CNN\n",
       "spark                0.849    0.948\n",
       "hdfs                 0.852    0.895\n",
       "mapreduce            0.849    0.960\n",
       "hadoop               0.867    0.964"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_acc_w2v = pd.DataFrame([\n",
    "                    [0.849, 0.948],\n",
    "                    [0.852, 0.895],\n",
    "                    [0.849, 0.960],\n",
    "                    [0.867, 0.964]\n",
    "                   ],\n",
    "             index=['spark','hdfs','mapreduce','hadoop'],\n",
    "             columns=pd.Index(['Traditional-CNN', 'DBR-CNN']))\n",
    "\n",
    "df_acc_w2v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1178cbb50>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HvP322wA89dRTDBo0qE6dmm/1ffv2rVO+YsWK9eoC9O3bl4suuoijjjqq0W2/+eabAOy0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1178c2590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_acc_w2v.plot(kind='bar', use_index = ['spark','hdfs','mapreduce','hadoop'], \\\n",
    "            rot = 360,ylim=(0,1.2), figsize=(6,5)).set_ylabel('Accuracy')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
